Poverty Reduction Nexus: The Case of the ASEAN - …...and the extension of economic integration so that each nation is able to stimulate FDI inflows and the degree of openness so

International Journal of Scientific & Engineering Research Volume 9, Issue 9, September-2018 1245 ISSN 2229-5518

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Globalisation – Poverty Reduction Nexus: The Case of the ASEAN

Chanhsy Samavonga

a Centre for Macroeconomic Policy and Economic Restructuring,

National Institute for Economic Research (NIER), Vientiane, Laos

Email: [email protected]

Abstract

The paper aims to examine the globalization-poverty reduction nexus in ASEAN countries.

The Human Development Index (HDI) and Gross Domestic Product per capita (PGDP) are

two key variables that measure welfare or poverty reduction. These variables used to explain

the impact of globalization on welfare which are foreign direct investment (FDI) inflows and

international trade. The models also use time dummy to capture the influence of the global

financial crisis during the 2008-2009 period. The paper finds that FDI inflows have a strong

positive and significant effect on poverty reduction, whereas the impact of international trade

on welfare is lower compared to inward FDI. The results also indicate that the effect of

inward FDI on welfare is greater in poorer countries than that for wealthier countries. From

the findings of this paper, three policy recommendations are made for ASEAN countries.

First, these countries should encourage more FDI from abroad to develop the economy due to

its benefits to welfare. Furthermore, trade policy also needs to be promoted so that ASEAN

countries can acquire gains from trade in order to benefit from welfare improvement even the

impact of international trade on welfare is lower than that of inward FDI. Finally, ASEAN

countries should promote the implementation of some economic and investment agreements,

and the extension of economic integration so that each nation is able to stimulate FDI inflows

and the degree of openness so as to achieve welfare improvement.

Keywords: ASEAN, globalization, FDI, international trade, poverty reduction, welfare.

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1. Introduction

The modern world economy and society have been rapidly globalizing in recent decades due

to social and economic integration. After increasing process of economic integration since the

1990s, international political economy and international relations have taken into account a

major question of how does globalization affects the poor and increases welfare improvement

(Nissanke and Thorbecke 2008). From time to time, many countries have formed regional as

well as global associations in order to benefit from globalization. For example, the

Association of Southeast Asian Nations (ASEAN) was established on 8th August 1967 in

Bangkok, Thailand and 10 countries are members of the association including Brunei

Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore,

Thailand, and Vietnam. The main purposes of the ASEAN are set out in the ASEAN

Declaration including accelerating economic growth, promoting regional peace and stability,

collaborating, providing assistance to each other, and other purposes (ASEAN, 2017). At the

present time, the key characteristics of the 2003 ASEAN Economic Community Declaration

had been reached by 2015 and obtained some achievements of the declaration of the 2000

Millennium Development Goals (MDGs). These achievements have necessarily contributed

to human and economic development as well as poverty reduction. The ASEAN formation

has been a sign of increasing globalization throughout the region over the last five decades.

Each member country not only has established a profound collaboration within the

association but also has broadened economic and cultural integration with other countries

around the world in order to develop its economy (Uttama 2015).

Globalization has become inevitable and played important roles in the global economy. On

the one hand, by participating in globalization, each country has opportunities to approach

international markets by exporting and importing its products, acquire advanced technology

transfer by FDI and other benefits. FDI, for example, has significantly contributed to

economic development and most countries would attract more FDI from overseas in order to

sustain economic growth and macroeconomic stability. Likewise, the significant increase in

FDI inflows has resulted from some empirical factors including increasing openness to trade

and improved business environment. The increase of inward FDI plays an important role in

reducing poverty in the ASEAN countries (Uttama 2017). On the other hand, globalization

has created winners and losers at numerous levels and also causes some negative effects such

as increasing inequality and global warming (Nissanke and Thorbecke 2008).

Over recent decades, real GDP per capita and HDI which are indicators to measure human

development have been improving in both developed and developing countries. These

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improvements seem to result in better welfare due to increasing HDI and GDP per capita. For

instance, welfare improvement has been achieved considerably in most countries in terms of

increasing HDI in the ASEAN. Figure 1 presents the improvement of HDI in the ASEAN

countries over the period from 2000 to 2015. As can be seen clearly from the graph, HDI has

gradually increased in 10 countries. Singapore and Brunei are the two richest countries and

remain on the top with the highest HDI in the region over the period. In contrast, the three

countries with the lowest HDI are Myanmar, Cambodia, and Lao PDR. All member countries

not only interact with each other in order to achieve the ASEAN’s goals, they also have a

social-economic relationship with other countries outside of the ASEAN as well as other

economic associations. According to The United Nations Development Program (UNDP

2017), the improvement of HDI in the ASEAN has been increasing over the period as can be

seen in the following diagram.

Figure 1: HDI in the ASEAN countries from 2000-2015.

Source: UNDP (2017).

Many countries around the world remain to be a developing country relative to low income.

This is due to several reasons such as the inefficient use of factor endowments, or a large

technological gap compared to developed economies. In addition, the number of people living

under the poverty line of $1 a day is more than one-sixth of the world population, and half of

developing countries live on less than $2 a day (Harrison 2007, cited in MacDonald &

Majeed 2010). However, there has been a significant achievement in poverty reduction in

developing countries, especially in ASEAN where economic integration has become

widespread and necessary recently. Taken GDP per capita in some countries as examples,

there was a substantial increase in GDP per capita from USD 98 in 1990 to USD 2,111 in

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Brunei Cambodia Indonesia Lao PDR Malaysia

Myanmar Philippines Singapore Thailand Vietnam

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2015 in Vietnam, and from USD 585 to more than USD 3,336 in Indonesia, from USD 203 to

nearly USD 2,159 in Lao PDR (World Bank, 2017).

Figure 2 shows that GDP per capita was gradually increased from 2000 to 2015 in most

ASEAN countries, but it fluctuated in the period from 2007 to 2010 with the advent of the

global financial crisis in 2008. The two countries like Singapore and Brunei, with the highest

HDI, also have the highest GDP per capita in the region. GDP per capita in these two

countries considerably rose, in Singapore, for instance, from more than USD 23,792 in 2000

to over USD 53,629 in 2015 and from around USD 18,000 to approximately USD 31,000 in

2015 in Brunei. According to the World Bank’s classification of income level, most nations

of the ASEAN are still classified as being of lower middle income, except for Singapore and

Brunei which belong to the high-income country group.

Figure 2: GDP per capita in the ASEAN countries from 2000-2015

Source: WB (2017).

Although most ASEAN countries remain to have low GDP per capita, the rate of people

living in poverty had considerably decreased over the last two decades. For instance,

according to the General Statistics Office of Vietnam (2017), Vietnam’s poverty rate had

rapidly declined from 58.1% in 1993 to 7% in 2015. More people have access to the higher

standard of living and other facilities such as infrastructure, education, healthcare, and

sanitation.

Economic integration is an important motivation in attracting FDI from abroad as well as to

gain access to international markets. For instance, FDI brings into host countries some

fundamental benefits in terms of capital formation, technology transfer, production know-

0.00

10000.00

20000.00

30000.00

40000.00

50000.00

60000.00

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Brunei Cambodia Indonesia Lao PDR Malaysia

Myanmar Philippines Singapore Thailand Vietnam

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how, management methods, marketing skills, information and so on. These factors should

bring several impacts into a host countries’ economy which might lead to poverty reduction.

In other words, welfare improvement has significantly resulted from globalization related to

international trade and FDI. Because of the achievements as discussed, it is necessary to

investigate the contribution of globalization to the improvement of welfare in Southeast Asian

countries. This research paper will examine the globalization-poverty reduction nexus by

measuring the effects of international trade and FDI inflows on welfare in ASEAN over the

2000-2015 period. The result of this paper will respond to two research questions: (1) What is

the impact of globalization in terms of FDI inflows and international trade on poverty

reduction in the ASEAN? (2) Does globalization affect welfare in poorer and richer countries

in the ASEAN differently?

To answer two research questions above, it is necessary to review some literature that many

researchers have studied the topic in recent years. Moreover, to response the questions

quantitatively, a panel data model using data related to globalization on the 10 ASEAN

countries from 2000 to 2015 will be estimated. Based on the efficient results of regressions,

the globalization-poverty reduction nexus in the ASEAN will be interpreted and discussed,

and policy recommendations also are introduced in order to promote the welfare improvement

resulted from globalization.

This paper will be organized as follow. First of all, section 2 will overview the relationship

between globalization and poverty reduction, particularly the effects of international trade and

FDI inflows on poverty reduction. Section 3 will present the methodology and data of this

paper. Some findings of the globalization-poverty reduction nexus, the effects of inward FDI

and international trade on welfare in ASEAN will be discussed in section 4. Finally, some

significant points and policy implications are concluded in the conclusion.

2. Literature review

The Human Development Index (HDI)

The United Nations Development Program’s (UNDP) HDI is a development index which is

used to emphasize that individuals and their capabilities would be the ultimate criteria to

assess a country’s social and economic development based on three dimensions of human

development consisting of a long and healthy life, knowledge, and a decent standard of living

(UNDP 2017).

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Figure 3: Human Development Index by UNDP

Source: UNDP (2017).

In recent decades, the HDI has been an almost accepted indicator to measure human

development and this index has been readily available for all countries. Thus, the HDI has

become an appropriate index to evaluate a country’s welfare, which could sufficiently reflect

human development and standard of living as well as people’s capabilities to access and

improve social welfare. Another indicator which is also to measure the standard of living as

well as the economic growth is GDP per capita. These two indicators have been broadly used

in many studies in order to assess poverty reduction as well as the improvement of welfare.

The measure of welfare improvement using HDI and GDP per capita

Many studies have used HDI and GDP per capita to measure welfare improvement in a

country or across countries. For example, Gohou and Soumaré (2012) employed HDI and real

per capita GDP as two indicators to measure impacts of inward FDI on welfare and its

regional differences in Africa. Using a panel data regression analysis, the authors argued that

FDI inflows have strong impacts on welfare improvement in Africa as a whole, and concludes

that FDI inflows result in a higher influence on poverty reduction in low-income countries

than richer nations. Muhammad et al. (2010) also examined whether FDI and international

trade would be effective tools to sustain economic stability and development. This study

applied HDI as a dependent variable to measure the effect of globalization in which economic

and financial liberalization under the open economy. He concluded that inward FDI has a

statistically significant effect on HDI, whereas the impact of real GDP on welfare is negative

and insignificant due to income inequality in the case of Pakistan.

Figueroa (2014) investigates the impact of globalization on welfare improvement in

developing countries in Central and South America. The author also applied HDI, per capita

GDP, life expectancy, and public expenditure on education as dependent variables to measure

human development. The HDI measures the human development as a whole, while per capita

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GDP, life expectancy, and educational factor stand for each dimension of HDI created by the

UNDP. The paper concluded that globalization has diverse impacts on welfare in the Central

and Latin America developing countries. This implies that globalization can have both

positive and negative relationship with human development, depending on the indicators used

to measure each aspect of globalization.

The relationship between globalization and poverty reduction

Globalization refers to an increasing level of interdependence among countries around the

world in terms of the exports and imports of commodities and services, labor and capital

movement, cultural, political, and military alliances (Rabbanee et al., 2010). It is argued that

globalization is a promoter of human development. Globalization also has a favorable impact

on education by transforming illiterate people into productive human resources and improving

awareness of the population. Improvement in education can lead to many social benefits such

as enhancement of living standard, healthcare, and reduction in infant and child mortality rate.

Furthermore, globalization can enable people to increase the quality of life through the

consumption of various kinds of goods and services from abroad due to product availability

including basic foods, pharmaceuticals, and clothing. Most countries can benefit from

globalization in terms of international trade, which can utilize the comparative advantage of

each country in order to increase growth and generate more employment and income

(Rabbanee et al., 2010). However, globalization is also an inhibitor of human development. It

is argued that trade liberalization requires participants to eliminate or reduce import tariffs,

then leads to reductions in government revenue, therefore the government will need to

increase other taxes so as to maintain budget balance. This will negatively affect the

disposable incomes of citizens. In addition, globalization also has a negative impact on

agriculture due to tough competition in which developing countries might find it difficult to

sell their agricultural products in international markets (Rabbanee et al., 2010).

Sapkota (2011) investigates the effects of globalization on welfare in terms of human and

gender development in addition to the impacts across world regions and income group

countries using the annual panel data of 124 developing countries over the nine-year period

from 1997 to 2006. The author uses the KFO index of globalization to measure the impacts of

globalization including economic, social, and political globalization. This index covers all

aspects which reflect the relationship between an economy with the rest of the world in terms

of globalization. The paper applies HDI, GDP per capita, and the Human Poverty index as

dependent variables to measure the improvement of welfare, while the KFO index of

globalization is used as an explanatory variable. The study concludes that globalization not

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only improves human and gender development but also significantly alleviate the level of

poverty across developing countries.

In addition, Tsai (1994) claimed that domestic market size and trade balance resulted from

globalization significantly contribute to economic growth besides to poverty reduction. The

relationship between openness, growth, and poverty is also explored in a specific country,

Taiwan. The paper examines how openness to trade which is the sum of the exports and

imports to GDP ratio, economic growth, and the roles of government contribute to poverty

reduction. There is no doubt that openness to international trade benefits the poor and helps to

alleviate poverty in Taiwan. Nevertheless, FDI inflows have an insignificant impact on

welfare improvement (Tsai, 2007). Another empirical finding also demonstrated that inward

FDI and economic growth have a significant endogenous relationship in developing countries

(Li & Liu, 2005).

The impact of globalization on welfare improvement across countries has been extensively

studied. The role of globalization in increasing inequality and poverty reduction has been

identified in economies with imperfect financial markets and it was argued that there has been

a negative and statistically significant effect of globalization on poverty in countries where

financial systems are relatively imperfect and developed (MacDonal & Majeed 2012). It is

claimed that the globalization and poverty have a complex relationship, which could be non-

linear and heterogeneous and relate to multifaceted channels. Globalization not only affects

economic growth and poverty reduction but also creates the winners and losers through

various channels which affect both vertical and horizontal inequality (Ravallion, 2004, cited

in Nissanke & Thorbecke, 2008). Furthermore, globalization in terms of trade and

technological openness, capital and labour mobility, and pro-poor institutions affects poverty

reduction. The change in globalization could lead to welfare improvement even the findings

are not conclusive (Nissanke & Thorbecke 2010, cited in Uttama 2015).

Figini and Santarelli (2006) employed a panel data model to investigate the relationship

between globalization and welfare improvement in developing countries. The authors apply

the degree of openness to trade, financial openness, and the role of government as proxies to

measure the impacts of globalization on poverty reduction. Their findings argue that trade

openness which is the sum of exports and imports to GDP ratio and public sector have a

negative relationship with the level of poverty, whereas financial openness which is the net

FDI inflows to gross capital formation ratio have positive effects on the absolute poverty.

According to Neutel and Heshmati (2006), globalization leads to poverty reduction and lower

level of income inequality using large sample data of 65 developing countries. The authors

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use various globalization indices consisting of economic integration, personal contacts,

connections of technologies, and political engagement to measure the effects of globalization

on welfare across developing countries.

Another research had investigated the relationship between international trade and poverty

alleviation and give recommendations on trade reforms. The trade reforms-poverty nexus is

diverse and complex. Trade liberalization can affect the poor through the price and

availability of goods and services. The finding suggests that trade reforms have a positive

effect on poverty reduction through improving employment and income for the poor. As a

result, it is important to provide assistance for the poor to participate in markets resulted from

trade liberalization. For example, the provision of infrastructure, technical assistance, credit,

and other types of training can allow the poor to benefit from market opportunities,

macroeconomic stability, and economic growth (Bannister & Thugge, 2001; Fane, 2006).

Many studies have investigated the impact of economic integration on poverty reduction in

Asia as well as the ASEAN countries in recent years. FDI has played an important role in

economic development and improvement of social welfare. For instance, inward FDI resulted

from economic integration has increased in this region for the last four decades and has led to

economic expansion and development of regional production networks (Hew 2006). In

addition, there is a positive significant relationship between inward FDI and poverty

alleviation in terms of both individual and spatial aspects in the ASEAN. The positive

significant relationship remains between poverty reduction and GDP growth, openness to

trade, and foreign debt, whereas financial and infrastructure factors have a negative

significant relationship with poverty reduction in the ASEAN. The results of the paper

conclude that welfare improvement in Southeast Asian countries is driven by FDI inflows and

economic integration (Uttama 2015).

Benefits from globalization

Participating in globalization can offer some benefits for each country to develop the

economy. With the open economy, each country is able to expand the markets for domestic

products through international trade and that country can import products that are not

produced domestically (Gohou & Soumaré 2012). These benefits allow a country to exploit

its comparative advantage in order to use physical, natural, and human capital more

efficiently in the economy.

The impact of globalization on poverty reduction in developing countries has recently become

a major concern for economists and policymakers. Some studies have identified the

importance of international trade and FDI inflows resulted from economic integration in

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developing economic growth and reducing poverty. It is argued that FDI inflows can

contribute to the improvement of welfare through the social and economic side. Regarding the

social side, inward FDI generates employment, improve managerial skills, and stimulate

technological progress which can support the governments in achieving the poverty reduction

goal. In terms of the economic side, recent studies suggest that human development resulted

from FDI inflows increases human capital which is considered a principal contributor to

economic growth (Gohou & Soumaré 2012).

To summarize what we discussed literature review of the globalization-poverty reduction

nexus which has become a crucial issue of various studies. There is no doubt that

globalization would have both positive and negative impact on welfare, but several studies

conclude that globalization in terms of FDI inflows and international trade could significantly

overcome poverty. It is also important to investigate the effect of globalization on welfare in a

region like the ASEAN so that each country could benefit more from increasing economic

integration. In the next part, methodology and data of this paper will be discussed so as to

answer two research questions scientifically and statistically.

3. Methodology and data

3.1. Data

Many studies apply panel data to investigate the effects of FDI inflows and trade on welfare

in a region or continent such as Africa, Southeast Asia, Central and South America (Gohou &

Soumaré 2012; Figueroa 2014; Uttama 2015). Likewise, this paper will apply a panel data of

10 countries in the ASEAN from the year 2000 to 2015. The data for FDI, trade, GDP, GDP

per capita, official exchange rate, the number of the Internet users, and inflation are collected

from the World Development Indicators (WDIs). The data on the number of enrolment to

population ratio is compiled from The United Nations Educational, Scientific and Cultural

Organization (UNESCO) database. The source of HDI data is from the Human Development

Report of the UNDP. Also, some other data are collected from a particular country such as the

data of the poverty rate from the General Statistics Office of Vietnam (GSO) in Vietnam. Due

to the limitation of the data, this paper only assesses the globalization-poverty reduction

nexus in the ASEAN over the period.

The literature has used various indicators to measure a country’s welfare. International trade

and FDI inflows have been widely used as proxies of globalization (Magdalena, 2014). In this

paper, the models will find out the empirical contributions of FDI inflows and international

trade to welfare improvement in the ASEAN by using some variables which relate to

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international and domestic factors. Those factors resulted from globalization affect HDI and

per capita GDP directly and indirectly. GDP per capita would capture the economic

dimension of welfare as it is indicated in HDI constructed by the UNDP. This variable also

captures the degree of economic development. There is no doubt that a greater degree of

economic development would result in an improvement of welfare or higher poverty

reduction in a country. However, welfare or poverty reduction not only depends on economic

factors, but also depends on other dimensions such as health care, quality of education, and

other factors as well. These factors could perfectly reflect all aspects of people’s living

conditions. Due to the limitation of data availability of poverty incidence in the ASEAN, the

paper will employ HDI as an empirical variable to measure the welfare as well as poverty

reduction. The model using HDI as a dependent variable will capture the direct effects of FDI

and international trade on poverty reduction in the ASEAN. Apart from HDI indicator, FDI

and international trade would indirectly affect welfare improvement through economic

dimension by using GDP per capita as a dependent variable in the model. Therefore, two

main variables which are FDI inflows and international trade will be used to reflect the

globalization-poverty reduction nexus.

In addition, two linear models are specified using HDI and per capita GDP as the dependent

variables. The models will contain both international and domestic factors capturing

globalization all ASEAN’s members. The international factors will be indicated by FDI

inflows, the volume of exports and imports of commodities and services, and the official

exchange rates. Domestic variables will be government expenditure, inflation, education, and

individuals using the Internet. To be specific, the explanatory variables are the FDI inflows to

GDP ratio, the sum of exports and imports to GDP ratio which stands for the degree of

openness of a country, the official exchange rate, education which is the number of enrolment

to population ratio, government expenditure, inflation, and the percentage of individuals using

the Internet in total population. Moreover, the control variables in the models consist of the

official exchange rate, education, government expenditure, inflation, and the Internet users.

The models also use time dummy as a variable to capture the effect of the global financial

crisis on welfare in the year 2008-2009. The time dummy variable will present the individual

effect of crisis for the year 2008 and 2009. Also, the time trend is added to the models so as to

investigate whether or not welfare improves over time. The description of the variables and

sources of data are presented in the following table.

Table 1: The variables: description and source of data

Variables Description Measurement Source of data

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Welfare

𝐻𝐷𝐼 Human Development Index Unit The Human Development Report

𝑃𝐺𝐷𝑃 Gross domestic product per capita Current $US The World Bank’s World Development Indicators (WDIs)

Globalization

𝐹𝐷𝐼𝐺𝐷𝑃 Inward foreign direct investment to GDP ratio Unit WDIs

𝑇𝑅𝐴𝐷𝐸 Sum of total export and import of goods and services to GDP ratio Unit WDIs

Control variables

𝐸𝑅 Official exchange rate $ Local/$US WDIs

𝐸𝐷𝑈 Number of enrolment to population ratio Unit

The United Nations Educational, Scientific and Cultural Organization database

𝐺𝑂𝑉𝐸𝑋𝑃 Government expenditure % of GDP WDIs

𝐼𝑁𝐹 Inflation or GDP deflator Annual % WDIs

𝐼𝑁𝑇𝐸𝑅 Individuals using the Internet % of population WDIs

𝑦2008 Time dummies 2008 1

𝑦2009 Time dummies 2009 1

𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 The trend of time over the period From 1 to 16

Source: Author.

3.2. Model specification

There are two main techniques to run a regression using panel data including fixed effects and

random effects model. The fixed effects model assumes that all unobserved factors such as

location, religion, culture and so on affect the dependent variable do not change over time. In

other words, the intercept which captures unobserved factors of the model will be different

across sections in a panel data and fixed over time, but the coefficients of the explanatory

variables change over time. Regarding the random effects model, this model assumes that the

unobserved effect is uncorrelated with all explanatory variables (Wooldridge, 2012). In order

to decide which technique is appropriate, a Hausman test is conducted to determine which

model will be used in the paper, fixed effects model or random effects model. In the equations

of the models, some variables will be in the logarithm form under regression to measure the

elasticity that captures the effects of explanatory variables on independent variables.

To measure the effects of inward FDI and international trade on welfare through direct and

indirect channels, two models are indicated as follow:

Model 1: The impact of globalization on welfare

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𝑊𝑒𝑙𝑓𝑎𝑟𝑒𝑖𝑡 = 𝑎1 + 𝑎2𝐹𝐷𝐼𝐺𝐷𝑃𝑖𝑡 + 𝑎3𝑇𝑅𝐴𝐷𝐸𝑖𝑡 + 𝑎4𝐹𝐷𝐼𝐺𝐷𝑃𝑖𝑡 ∗ 𝐷𝑢𝑚2008

+ 𝑎5𝐹𝐷𝐼𝐺𝐷𝑃𝑖𝑡 ∗ 𝐷𝑢𝑚2009 + 𝑎6𝑇𝑅𝐴𝐷𝐸𝑖𝑡 ∗ 𝐷𝑢𝑚2008 + 𝑎7𝑇𝑅𝐴𝐷𝐸𝑖𝑡

∗ 𝐷𝑢𝑚2009 + 𝑎8�𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑣𝑎𝑟 + 𝑢𝑖𝑡

To further investigate the impact of globalization on welfare in terms of income level

differences, it is necessary to consider the following regression equation:

Model 2: The impact of globalization on welfare in terms of income group countries

𝑊𝑒𝑙𝑓𝑎𝑟𝑒𝑖𝑡 = 𝛽1 + 𝛽2𝐹𝐷𝐼𝐺𝐷𝑃𝑖𝑡 ∗ 𝐷𝑢𝑚𝐿𝑀𝐼𝐶 + 𝛽3𝑇𝑅𝐴𝐷𝐸𝑖𝑡 ∗ 𝐷𝑢𝑚𝐿𝑀𝐼𝐶 + 𝛽4𝐹𝐷𝐼𝐺𝐷𝑃𝑖𝑡

∗ 𝐷𝑢𝑚𝑈𝑀𝐼𝐶 + 𝛽5𝑇𝑅𝐴𝐷𝐸𝑖𝑡 ∗ 𝐷𝑢𝑚𝑈𝑀𝐼𝐶 + 𝛽3�𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑣𝑎𝑟 + 𝑢𝑖𝑡

The dummy variables depict the income level classified by the WB including lower-middle

income country (𝐿𝑀𝐼𝐶), upper-middle income country (𝑈𝑀𝐼𝐶). According to the income

level classification, Brunei and Singapore are high-income countries, so it is appropriate to

group these two countries into the 𝑈𝑀𝐼𝐶 country. Moreover, there are no low-income

countries in the ASEAN. The dummy variable for an income group will take the value of one

when a country belongs to that income level and zero otherwise.

It is expected that globalization results in welfare improvements through the impacts of

inward FDI and international trade. FDI inflows are expected to have positive effects on

poverty reduction. FDI will bring into the host countries a package of capital, advanced

technology, production know-how, management methods, and information. Therefore, inward

FDI will produce some positive effects on the host countries’ economy in promoting and

improving the economic development and social welfare. International trade also a

determinant of FDI inflows, that advantages to promote FDI inflows in a country.

International trade is also expected to positively lead to an improvement of poverty reduction.

A widely open economy is able to expand the markets for its production along with approach

of various products from international markets to satisfy the demand of domestic consumers.

Control variables are included in the study of globalization-poverty reduction nexus because

these variables have significant impacts on welfare. We expect government expenditure to

improve welfare because fiscal policies will affect the economic performance, infrastructure

system, education, and health which are reflected in HDI. In addition, the official exchange

rate also is expected to have a positive impact on welfare. In terms of education, the higher

the number of the population go to school, the more improvement in poverty reduction will

be. Furthermore, inflation is a control variable to capture the macroeconomic stability.

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Because high inflation increases the price of basic goods, it is expected that inflation will

negatively affect welfare, and will deleteriously affect the poor due to increases in the cost of

consumption. Another control variable is the number of the Internet users. This variable is a

proxy for the development of the infrastructure system. A better infrastructure system will

contribute to better living conditions, so this factor is expected to have a positive impact on

poverty reduction. Moreover, the global financial crisis in the year 2008-2009 is also expected

to have some negative effects on poverty reduction. This crisis was likely to deteriorate the

flows of FDI and transactions in the international goods markets and therefore will negatively

affect the poor or welfare improvement. Welfare improvement also is expected to increase

over time from 2000 to 2016.

3.3. Diagnostic tests

In econometric models, some problems of statistic results would be inevitable. The problems

would cause the results of regressions to be biased, inconsistent, and inefficient. Thus, there

would be a problem of heteroskedasticity over cross-sections in the panel data. An appropriate

method will be applied to test for the problem is the modified Wald test for group-wise

heteroskedasticity in the residuals of the fixed effects regression models. Moreover, there will

be a presence of serial correlations in time series of the panel data. To test for the problem of

autocorrelation in the fixed effects models, the Wooldridge test will be applied.

It is important to test for the problem of a unit root in the panel data. One way used to test the

variables separately is the Dickey-Fuller test, but this method would not be appropriate in

panel data with many variables. Another efficient method of testing for the presence of a unit

root in a variable that varies over the cross-sections in the panel data is Levin, Lin, and Chu

(2002) test (LLC). Instead of a number of panel unit root tests for many variables, LLC test

will base on the panel unit root regression which is the panel analogue of the augmented

Dickey-Fuller regression equation.

After testing for the problems of models with panel data, the models will be corrected by

appropriate methods to obtain the unbiasedness, consistency, and efficiency of the estimates.

Some methods would be appropriate to estimate the fixed effects models by OLS in which the

Newey-West robust method and robust standard errors will be applied to account for

heteroskedasticity and autocorrelation.

4. Empirical results and discussions

The aim of this paper is to investigate the globalization-poverty reduction nexus in ASEAN in

terms of the effects of inward FDI and international trade on welfare. The paper addresses two

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research questions: (1) What is the impact of globalization in terms of FDI inflow and

international trade on poverty reduction in ASEAN? (2) Does globalization affect welfare in

poorer and richer countries in ASEAN differently?

4.1. Descriptive statistics and testing

It is important to explore the data before analysing the results of fixed effects regression

models. There are 160 observations in the panel data including the 16-year period of 10

countries in the ASEAN.

Table 2: Descriptive statistics of the variables

𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝑶𝒃𝒔𝒆𝒓𝒗𝒂𝒕𝒊𝒐𝒏 𝑴𝒆𝒂𝒏 𝑺𝒕𝒂𝒏𝒅𝒂𝒓𝒅 𝑫𝒆𝒗 𝑴𝒊𝒏 𝑴𝒂𝒙

𝐻𝐷𝐼 160 0.663 0.131 0.412 0.925

𝑃𝐺𝐷𝑃 160 8,839.876 14,324.99 138.925 56,336.07

𝐹𝐷𝐼𝐺𝐷𝑃 160 0.049 0.053 0.0006 0.265

𝑇𝑅𝐴𝐷𝐸 160 1.279 0.976 0.002 4.416

𝐸𝑅 160 4,068.06 5,886 1.25 21,697.57

𝐺𝑂𝑉𝐸𝑋𝑃 160 11.645 5.411 3.46 29.4

𝐼𝑁𝐹 160 6.051 7.547 -22.091 41.51

𝐼𝑁𝑇𝐸𝑅 160 23.37 23.93 0.0002 82.103

𝐸𝐷𝑈 160 0.211 0.041 0.133 0.263

Source: Author estimation.

According to the description of the data presented in table 2, there is a larger gap between the

𝑚𝑖𝑛 and 𝑚𝑎𝑥 of 𝐻𝐷𝐼 and 𝑃𝐺𝐷𝑃, which are two dependent variables and the differences

between these two variables can reflect a vast disparity in the standard of living and human

development among all members over the 16-year period. Table 3 presents the correlation

matrix for 10 countries from 2000 to 2015, which reflects the relationships between the

variables.

As can be seen from the correlation matrix table, 𝐻𝐷𝐼 is highly correlated with 𝑃𝐺𝐷𝑃 of 80

percent. These two variables are proxies of welfare and the relationship demonstrates that

higher GDP per capita leads to higher standard of living and human development. As can be

seen from the table the FDI inflow to GDP ratio (𝐹𝐷𝐼𝐺𝐷𝑃) and the degree of openness

(𝑇𝑅𝐴𝐷𝐸) affect 𝐻𝐷𝐼 and GDP per capita. Thus, it is expected that these two variables could

be the main determinants of welfare development in ASEAN countries. We also observe that

the number of the Internet users (𝐼𝑁𝑇𝐸𝑅) is highly correlated with 𝐻𝐷𝐼 and 𝑃𝐺𝐷𝑃 of 85

percent and 78 percent respectively. This indicates that more access to the Internet or

improvement of infrastructure system may result in better welfare. Furthermore, 𝑇𝑅𝐴𝐷𝐸 and

𝐹𝐷𝐼𝐺𝐷𝑃 have a highly positive relationship at nearly 80 percent. It may be argued that

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𝑇𝑅𝐴𝐷𝐸 not only improves welfare but also is a determinant which attracts 𝐹𝐷𝐼 𝑖𝑛𝑓𝑙𝑜𝑤𝑠 in a

country.

Table 3: Correlation matrix for the ASEAN countries from 2000 to 2015

𝐻𝐷𝐼 𝑃𝐺𝐷𝑃 𝐹𝐷𝐼𝐺𝐷𝑃 𝑇𝑅𝐴𝐷𝐸 𝐸𝑅 𝐸𝐷𝑈 𝐺𝑂𝑉𝐸𝑋𝑃 𝐼𝑁𝐹 𝐼𝑁𝑇𝐸𝑅

𝐻𝐷𝐼 1.000

𝑃𝐺𝐷𝑃 0.802 1.000

𝐹𝐷𝐼𝐺𝐷𝑃 0.412 0.604 1.000

𝑇𝑅𝐴𝐷𝐸 0.643 0.639 0.796 1.000

𝐸𝑅 -0.326 -0.364 -0.089 -0.164 1.000

𝐸𝐷𝑈 -0.035 -0.127 -0.475 -0.348 -0.300 1.000

𝐺𝑂𝑉𝐸𝑋𝑃 0.374 0.326 -0.176 -0.159 -0.558 0.129 1.000

𝐼𝑁𝐹 -0.334 -0.235 -0.189 -0.329 0.226 -0.113 -0.115 1.000

𝐼𝑁𝑇𝐸𝑅 0.852 0.781 0.517 0.685 -0.258 -0.136 0.220 -0.356 1.000


Hausman test:

In panel data regression, there are different methods to estimate the regression, particularly

fixed effects and random effects method. In order to decide which method is appropriate to

estimate the models in this paper, Hausman tests are conducted. The null hypothesis is the

random effects estimator which is consistent and efficient. The alternative hypothesis is the

fixed effects estimator which is still consistent, but not efficient due to misspecification of the

structure of the covariance matrix. Formally the hypotheses are:

𝐻0: 𝑅𝑎𝑛𝑑𝑜𝑚 𝑒𝑓𝑓𝑒𝑐𝑡𝑠 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑜𝑟

𝐻𝑎: 𝐹𝑖𝑥𝑒𝑑 𝑒𝑓𝑓𝑒𝑐𝑡𝑠 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑜𝑟

According to the Hausman tests, the p-values of 0.0000 in the test of HDI and 0.0000 in the

test of 𝑃𝐺𝐷𝑃 indicate strong rejection of the null hypothesis. Therefore, the fixed effects

model is appropriate to use to estimate the panel data models in this research. The fixed

effects model allows the intercepts to vary over cross-sections, while the coefficients of

explanatory variables (the slopes) will be assumed to be the same. In addition, fixed effects-

linear models will be used and run by the method of Ordinary Least Squares (OLS).

Testing for heteroskedasticity and autocorrelation:

The problems of heteroskedasticity and autocorrelation are common in the econometric

models. In panel data, these problems might appear in the regressions and lead to inefficient

estimators. Due to these problems, the estimators are still unbiased but not efficient, and the

OLS method computes standard errors using incorrect formulas. To test for the presence of

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heteroskedasticity, the Modified Wald test for group-wise heteroskedasticity in the fixed

effect regression model is applied. The Wald tests for the regressions of 𝐻𝐷𝐼 and 𝑃𝐺𝐷𝑃, p-

values of the tests are strongly significant. These p-values indicate that there is a presence of

heteroskedasticity in the regressions of the models. Similarly, the Wooldridge test for

autocorrelation in panel data is used to investigate the problems in terms of time series in a

section of panel data. The p-values of 0.0000 in the tests also state that there is a presence of

autocorrelation in the regressions of the models. Therefore, it is necessary to correct the

problems of heteroskedasticity and autocorrelation in order to obtain statistically significant

results of regressions by using OLS standard error robust method and the Newey-West

standard error to estimate the fixed effects models.

Testing for the unit root:

It is important to test for the stationary of the variable 𝐻𝐷𝐼, 𝑃𝐺𝐷𝑃, 𝐹𝐷𝐼𝐺𝐷𝑃, and 𝑇𝑅𝐴𝐷𝐸.

One way to test whether or not there is an existence of a unit root in the data series of the

variables is Levin, Lin, and Chu (2002) test (𝐿𝐿𝐶 𝑡𝑒𝑠𝑡). The results of the test are in the

following table:

Table 4: Levin, Lin, and Chu (2002) test for stationary

𝑽𝒂𝒓𝒊𝒂𝒃𝒍𝒆 𝒕 𝒔𝒕𝒂𝒕𝒊𝒔𝒕𝒊𝒄𝒔 𝒑 − 𝒗𝒂𝒍𝒖𝒆

𝐻𝐷𝐼 - 5.343 0.0000

l𝑜𝑔𝑃𝐺𝐷𝑃 - 3.639 0.0001

𝐹𝐷𝐼𝐺𝐷𝑃 -2.813 0.0025

𝑙𝑜𝑔𝐹𝐷𝐼𝐺𝐷𝑃 - 2.823 0.0024

𝑇𝑅𝐴𝐷𝐸 -1.190 0.1171

𝑙𝑜𝑔𝑇𝑅𝐴𝐷𝐸 -1.191 0.1168

Source: Author.

The results in table 3 reject the existence of unit roots for three variables 𝐻𝐷𝐼, 𝑃𝐺𝐷𝑃, and

𝐹𝐷𝐼𝐺𝐷𝑃. In other words, these variables are stationary. However, the variable 𝑇𝑅𝐴𝐷𝐸

contains a unit root in terms of both linear and logarithm form with a p-value of 0.1171 and

0.1168 respectively. The significance level of the 𝐿𝐿𝐶 𝑡𝑒𝑠𝑡 on the unit root of variable

𝑇𝑅𝐴𝐷𝐸 is almost 10 percent, so the unit root of 𝑇𝑅𝐴𝐷𝐸 would not be a major problem in the

time series of a short period of time. In the next part, the empirical results derived from robust

regressions will be discussed in order to measure the effects of globalization on welfare in the

ASEAN.

4.2. The impact of FDI inflow and international trade on welfare

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To examine the impact of globalization on welfare, the equation of model 1 is applied to run

the regressions. Table 5 gives the panel regression results to measure the effects of inward

FDI and international trade on poverty reduction using 𝐻𝐷𝐼 as the dependent variables for

welfare. Column (1) and (2) present the effects of 𝐹𝐷𝐼𝐺𝐷𝑃 and 𝑇𝑅𝐴𝐷𝐸 on HDI with time

trend as a control variable. The results show that the 𝐹𝐷𝐼 to 𝐺𝐷𝑃 ratio positively affects

welfare at a significance level of 5 percent, and the impact of 𝑇𝑅𝐴𝐷𝐸 on welfare is also

positive and significant at 5 percent level, but lower than that of 𝐹𝐷𝐼𝐺𝐷𝑃. In columns (3) to

(5), some control variables are added to the model including 𝑔𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒,

𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛, 𝑒𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑟𝑎𝑡𝑒, 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, 𝑡ℎ𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑢𝑠𝑒𝑟𝑠, dummy, and time trend. The

results indicate that 𝐹𝐷𝐼𝐺𝐷𝑃 has a positive effect on welfare and more statistically significant

and greater than 𝑇𝑅𝐴𝐷𝐸 variable. Columns (6) and (7), the model is estimated for 𝐹𝐷𝐼𝐺𝐷𝑃

and 𝑇𝑅𝐴𝐷𝐸 separately. The results demonstrate the effect of 𝐹𝐷𝐼 𝑖𝑛𝑓𝑙𝑜𝑤𝑠 on welfare remain

greater than 𝑇𝑅𝐴𝐷𝐸 and significant at 1 percent level. It is can be argued that 𝑖𝑛𝑤𝑎𝑟𝑑 𝐹𝐷𝐼 is

a major determinant that can remarkably reduce poverty in the ASEAN, whereas international

trade also has a positive significant effect, but the effect of trade on welfare is lower than

𝐹𝐷𝐼.

In the last two columns, the model is estimated by using the Newey-West method in terms of

the impact of 𝐹𝐷𝐼𝐺𝐷𝑃 on welfare with the absence of 𝑇𝑅𝐴𝐷𝐸 and the impact of 𝑇𝑅𝐴𝐷𝐸 on

welfare without 𝐹𝐷𝐼𝐺𝐷𝑃. As can be seen from the columns (8) and (9), 𝐹𝐷𝐼𝐺𝐷𝑃 has a

positive impact on welfare at 5 percent of significance level. Similarly, the effect of

𝑇𝑅𝐴𝐷𝐸 on 𝐻𝐷𝐼 is significant at 1 percent level but much lower than 𝐹𝐷𝐼𝐺𝐷𝑃. Therefore,

𝐹𝐷𝐼 𝑖𝑛𝑓𝑙𝑜𝑤𝑠 become a significant factor contributing to economic development as well as

welfare improvement in the ASEAN countries.

In terms of the effects of some control variables on welfare, the impact of 𝐺𝑂𝑉𝐸𝑋𝑃 is

insignificant and negative in some cases. 𝐼𝑁𝑇𝐸𝑅 plays an important role in economic

development, but this factor has a negative impact on welfare in this model and statistically

significant at 1 percent level. The impacts of the official exchange rate, education, and

inflation on welfare are low and not statistically significant in this model even it is expected

that 𝐸𝑅 and 𝐸𝐷𝑈 would have a high impact on the improvement of welfare.

To capture the impact of the GFC during 2008-2009 on welfare, the dummy is added to the

model for the years 2008 and 2009. The GFC could negatively affect the economies around

the world. The poor would be affected due to increases in the price of basic goods and

services. With the time dummy, the intercepts of the model will be different among the period

without crisis and period including a shock of the financial crisis. As can be seen from table 5,

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column (5) depicts the effect of 𝐹𝐷𝐼 𝑖𝑛𝑓𝑙𝑜𝑤𝑠 and trade on poverty reduction in the case of

controlling for all variables. The financial crisis has a negative effect on welfare in 2008 in

terms of 𝐹𝐷𝐼𝐺𝐷𝑃 variable. The effect of trade on welfare during the crisis is positive.

However, these impacts of inward 𝐹𝐷𝐼 and trade on poverty reduction are insignificant.

Interestingly, 𝐻𝐷𝐼 or welfare improvement increases over the period at 1 percent significance

level.

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Table 5: Panel regression results for the impact of globalization on HDI

𝑯𝑫𝑰 (1) (2) (3) (4) (5) (6) (7) (8) (9) 𝐼𝑁𝑇𝐸𝑅𝐶𝐸𝑃𝑇 0.600***

(0.006) 0.575*** (0.012)

0.574*** (0.012)

0.56*** (0.015)

0.588*** (0.032)

0.591*** (0.031)

0.595*** (0.031)

0.421*** (0.064)

0.292*** (0.071)

𝐹𝐷𝐼𝐺𝐷𝑃 0.134** (0.059)

0.134** (0.05)

0.136** (0.049)

0.111*** (0.028)

0.115*** (0.033)

0.271** (0.138)

𝐹𝐷𝐼𝑦2008 0.105 (0.082)

-0.024 (0.083)

-0.069 (0.123)

-0.092 (0.078)

0.082 (0.077)

0.205 (0.249)

𝐹𝐷𝐼𝑦2009 0.083** (0.034)

0.05 (0.08)

0.07 (0.108)

-0.009 (0.071)

0.075*** (0.023)

0.154 (0.225)

𝑇𝑅𝐴𝐷𝐸 0.023** (0.009)

0.02** (0.009)

0.017* (0.008)

0.021** (0.007)

0.025*** (0.006)

0.051*** (0.013)

𝑇𝑅𝐴𝐷𝐸𝑦2008 -0.0003 (0.001)

0.003 (0.003)

0.004 (0.004)

0.004 (0.003)

-0.001 (0.001)

-0.0001 (0.007)

𝑇𝑅𝐴𝐷𝐸𝑦2009 0.002*** (0.001)

0.003 (0.003)

0.002 (0.004)

0.004 (0.003)

0.003*** (0.001)

0.007 (0.007)

𝐸𝑅 3.48e-07 (0.000)

2.02e-06 (0.000)

-1.84e-07 (0.000)

2.41e-06* (0.000)

3.38e-06** (0.000)

𝐸𝐷𝑈 -0.054 (0.12)

0.014 (0.122)

-0.081 (0.114)

0.397** (0.208)

0.571*** (0.19)

𝐺𝑂𝑉𝐸𝑋𝑃 0.001 (0.001)

-0.0001 (0.001)

0.0002 (0.0006)

-0.0003 (0.001)

0.007*** (0.002)

0.009*** (0.002)

𝐼𝑁𝐹 0.0003 (0.0003)

0.0002 (0.0001)

0.0002* (0.0001)

0.0002* (0.0001)

-0.001 (0.001)

0.0004 (0.001)

𝐼𝑁𝑇𝐸𝑅 -0.001*** (0.0002)

-0.001*** (0.0002)

-0.001*** (0.0002)

0.005*** (0.0003)

0.003*** (0.0005)

𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 0.007*** (0.001)

0.007*** (0.001)

0.007*** (0.001)

0.006*** (0.001)

0.009*** (0.001)

0.009*** (0.001)

0.01*** (0.001)

-0.005*** (0.002)

-0.002 (0.002)

𝑁𝑜 𝑂𝑏𝑠. 160 160 160 160 160 160 160 160 160 𝐹 − 𝑆𝑡𝑎𝑡 32.82 37.39 35.76 1229.25 216.55 546.83 111.42 53.48 62.39 𝑅2 0.8946 0.8994 0.9062 0.9124 0.9496 0.9382 0.9457

Notes: To account for heteroscedasticity and autocorrelations, we use robust standard error method to run fixed effect models in the columns from (1) to (7). The two last columns (8, 9) use the Newey-West robust method. Standard errors are in parentheses. ***1% significance level, **5% significance level, *10% significance level.


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With the same panel and regression, GDP per capita is applied as a dependent variable to

measure welfare. Table 6 indicates the panel regression results which reflect the impact of

globalization on GDP per capita. The regressions are estimated with and without control

variables as well as controlling for the impact of the global financial crisis through the time

dummy. In terms of using only time trend as a control variable (columns (1) and (2)),

𝐹𝐷𝐼𝐺𝐷𝑃 and 𝑇𝑅𝐴𝐷𝐸 have a positive effect on 𝑃𝐺𝐷𝑃 at 1 percent significance level. From

column (3) to (5), the regression is estimated with control variables. The impact of 𝐹𝐷𝐼𝐺𝐷𝑃

on 𝑃𝐺𝐷𝑃 is positive and statistically significant at the 5 percent level, whereas the impact of

𝑇𝑅𝐴𝐷𝐸 becomes insignificant. In columns (6) and (8), the model regress with control

variables without 𝑇𝑅𝐴𝐷𝐸, the effect of 𝐹𝐷𝐼𝐺𝐷𝑃 on GDP per capita remains positive and

statistically significant at 1 percent and 5 percent level for two different methods. The model

is estimated without 𝐹𝐷𝐼𝐺𝐷𝑃 in columns (7) and (9) to measure the impact of international

trade on poverty reduction. The results show that the impact of 𝑇𝑅𝐴𝐷𝐸 on welfare is

statistically positive. However, this impact is negative and significant at 1 percent level in the

years of the crisis. These results state an increase in 𝐹𝐷𝐼 to 𝐺𝐷𝑃 𝑟𝑎𝑡𝑖𝑜 and a high degree of

openness in ASEAN would lead to an increase in GDP per capita, therefore poverty will be

improved. Moreover, the crisis negatively affected welfare improvement in the ASEAN.

Regarding the impacts of some control variables on GDP per capita, the 𝐸𝑅, 𝐸𝐷𝑈, and 𝐼𝑁𝐹

have statistically positive effects on 𝑃𝐺𝐷𝑃 but they are insignificant. From the columns (5) to

(7), the model is estimated with robust standard errors method. The results show the effects

of government spending and infrastructure system are negative but insignificant and the

effects are not as the same as the expectation of these variables on welfare improvement.

Nonetheless, in columns (8) and (9), the effect of 𝐺𝑂𝑉𝐸𝑋𝑃 and 𝐼𝑁𝑇𝐸𝑅 are positive and

significant at the 1 percent level under the Newey – West standard error method. As a result,

government spending and improvement of infrastructure system not only increases the 𝐻𝐷𝐼

but also leads to an increase in GDP per capita. Thus, an increase in government expenditure

and infrastructure improvement could significantly contribute to poverty reduction in the

region.

In brief, the impact of FDI inflows and international trade on welfare are positive and

significant, but their impacts on poverty reduction are negative during the crisis. When the

inward FDI to GDP ratio increases by one unit holding others constant, the 𝐻𝐷𝐼 increases by

over 0.1 unit. For GDP per capita, when 𝐹𝐷𝐼𝐺𝐷𝑃 rises by 1 percent, the GDP per capita

increases by around 0.05 percent. 𝑇𝑅𝐴𝐷𝐸, on the other hand, the impact is much lower in

terms of 𝐻𝐷𝐼 only 0.02 unit, and larger in terms of 𝑃𝐺𝐷𝑃 about 0.08 percent.

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Table 6: Panel regression results for the impact of globalization on 𝑃𝐺𝐷𝑃

𝒍𝒐𝒈𝑷𝑮𝑫𝑷 (1) (2) (3) (4) (5) (6) (7) (8) (9) 𝐼𝑁𝑇𝐸𝑅𝐶𝐸𝑃𝑇 7.255***

(0.118) 7.039*** (0.083)

7.256*** (0.115)

6.989*** (0.17)

7.819*** (1.393)

8.009*** (0.018)

7.946*** (1.453)

8.496*** (0.695)

6.78*** (0.612)

𝑙𝑜𝑔𝐹𝐷𝐼𝐺𝐷𝑃 0.068*** (0.021)

0.058** (0.024)

0.055** (0.021)

0.045** (0.019)

0.05** (0.018)

0.197*** (0.072)

𝑙𝑜𝑔𝐹𝐷𝐼𝑦2008 -0.042*** (0.006)

-0.039*** (0.007)

-0.042*** (0.01)

-0.035** (0.013)

-0.038*** (0.011)

-0.131*** (0.054)

𝑙𝑜𝑔𝐹𝐷𝐼𝑦2009 -0.013* (0.008)

-0.008 (0.011)

-0.008 (0.014)

-0.002 (0.016)

-0.008 (0.010)

-0.045 (0.052)

𝑙𝑜𝑔𝑇𝑅𝐴𝐷𝐸 0.088*** (0.019)

0.085*** (0.017)

0.053 (0.046)

-0.008 (0.135)

0.046 (0.131)

0.407*** (0.069)

𝑙𝑜𝑔𝑇𝑅𝐴𝐷𝐸𝑦2008 -0.065*** (0.006)

-0.046*** (0.003)

-0.049*** (0.009)

-0.027* (0.016)

-0.046*** (0.012)

-0.154*** (0.065)

𝑙𝑜𝑔𝑇𝑅𝐴𝐷𝐸𝑦2009 -0.08*** (0.007)

-0.072*** (0.008)

-0.075*** (0.014)

-0.052*** (0.017)

-0.06** (0.022)

-0.19*** (0.070)

𝑙𝑜𝑔𝐸𝑅 0.079 (0.129)

0.066* (0.036)

0.033 (0.131)

0.014 (0.037)

-0.129*** (0.033)

𝑙𝑜𝑔𝐸𝐷𝑈 0.637 (0.643)

0.673 (0.620)

0.653 (0.666)

1.161*** (0.385)

-0.001 (0.339)

𝐺𝑂𝑉𝐸𝑋𝑃 0.02 (0.015)

-0.0002 (0.019)

-0.004 (0.020)

-0.004 (0.020)

0.083*** (0.021)

0.071*** (0.019)

𝐼𝑁𝐹 0.002 (0.007)

0.002 (0.006)

0.001 (0.007)

0.003 (0.005)

-0.01 (0.013)

0.015* (0.008)

𝐼𝑁𝑇𝐸𝑅 -0.01* (0.006)

-0.01 (0.006)

-0.011* (0.006)

0.054*** (0.005)

0.027*** (0.005)

𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 0.101*** (0.01)

0.102*** (0.009)

0.099*** (0.009)

0.098*** (0.007)

0.123*** (0.016)

0.125*** (0.017)

0.13*** (0.016)

-0.043** (0.019)

0.034** (0.016)

𝑁𝑜 𝑂𝑏𝑠. 160 160 160 160 160 160 160 160 160 𝐹 − 𝑆𝑡𝑎𝑡 53.34 1171.34 15647.31 5.29e+06 117.91 510.92 2595.45 50.93 78.24 𝑅2 0.8754 0.8805 0.8887 0.8919 0.9111 0.9084 0.9061

Notes: To account for heteroscedasticity and autocorrelations, we use robust standard error method to run fixed effect models in the columns from 1 to 7. The two last columns (8, 9) use the Newey-West robust method. Standard errors are in parentheses. ***1% significance level, **5% significance level, *10% significance level.

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4 .3. The impact of globalization on welfare across income groups

To address the second research question, what is the impact of globalization on welfare across

different income groups in ASEAN? Using lower-middle income group countries and upper-

middle income countries as dummy variables is a way to capture the differences in the impact

of inward FDI and trade on welfare across countries. The results are estimated by the robust

standard error method and the Newey-West method with and without control variables.

Similarly, using time dummy as a control variable in this model to capture the impact of the

GFC on poverty reduction. Firstly, the impacts of FDI inflows and international trade on

welfare using 𝐻𝐷𝐼 as the dependent variable are presented in table 7.

Table 7 presents the regression results for ASEAN across income groups. The impact of

𝐹𝐷𝐼𝐺𝐷𝑃 on 𝐻𝐷𝐼 is positive and statistically significant at 5 percent and 10 percent level.

From the columns (1) to (4), the impact of FDI inflows on welfare in lower-middle income

countries is greater than that of richer countries. However, the effect of inward FDI in poorer

countries is smaller than that of richer countries, but it is insignificant (column (5)).

Consequently, FDI inflows play a significant role in promoting economic growth and

contribute to poverty reduction in poorer countries in comparison with richer countries in the

ASEAN. In terms of 𝑇𝑅𝐴𝐷𝐸 variable, the impact of trade on welfare in poorer countries is

larger and more statistically significant than that of upper-middle-income countries. As can be

seen from column (5), with the presence of the GFC, inward FDI has a negative effect on

𝐻𝐷𝐼 in upper-middle income countries, but the impact is positive in a lower-middle income

group. Regarding trade, the impact of trade on welfare is the opposite, which is negative in

poorer countries, but positive in richer countries. Thus, the financial crisis of 2008-2009

indirectly led to some negative effects on welfare across the ASEAN through its impact on

inward FDI and international trade.

The effects of control variables such as official exchange rate, education, government

spending, and the number of the Internet users on welfare are negative and insignificant in the

columns (5) to (7). Nevertheless, these impacts become positive and significant at 1 percent

significant level in the model estimated for 𝐹𝐷𝐼𝐺𝐷𝑃 and 𝑇𝑅𝐴𝐷𝐸 separately (column (8) and

(9)). Thus, government spending and improvement of infrastructure play important roles in

reducing poverty through fiscal policies supporting the poor and facilitating living conditions.

The results also show that 𝐻𝐷𝐼 increases over time. It can be concluded that the ASEAN had

achieved some improvements in poverty reduction over the period due to increasing

globalization and implementation of each country’s economic policy.

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Table 7: Panel regression results for the impact of globalization on 𝐻𝐷𝐼 across income groups

𝑯𝑫𝑰 (1) (2) (3) (4) (5) (6) (7) (8) (9) 𝐼𝑁𝑇𝐸𝑅𝐶𝐸𝑃𝑇 0.599***

(0.005) 0.578*** (0.012)

0.581*** (0.010)

0.567*** (0.012)

0.589*** (0.026)

0.589*** (0.032)

0.596*** (0.026)

0.376*** (0.060)

0.294*** (0.070)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶 0.334** (0.117)

0.243* (0.134)

0.217* (0.123)

0.051 (0.077)

0.203** (0.074)

-1.011*** (0.356)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶 0.079 (0.069)

0.089 (0.079)

0.107* (0.059)

0.148*** (0.034)

0.136** (0.048)

0.699*** (0.159)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶𝑦2008 -0.021 (0.067)

-0.011 (0.203)

0.035 (0.251)

0.195** (0.071)

-0.029 (0.039)

0.601* (0.362)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶𝑦2009 0.034 (0.069)

0.09 (0.209)

0.221 (0.207)

0.239* (0.115)

0.005 (0.025)

0.153 (0.481)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶𝑦2008 0.212 (0.190)

-0.629 (0.682)

-0.861 (0.581)

-1.207*** (0.372)

0.36* (0.205)

1.238** (0.511)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶𝑦2009 0.075 (0.060)

0.032 (0.063)

-0.003 (0.063)

-0.027 (0.068)

0.132*** (0.035)

0.195*** (0.144)

𝑇𝑅𝐴𝐷𝐸𝐿𝑀𝐼𝐶 0.036** (0.015)

0.028* (0.014)

0.024* (0.014)

0.043*** (0.009)

0.048*** (0.007)

-0.001 (0.025)

𝑇𝑅𝐴𝐷𝐸𝑈𝑀𝐼𝐶 0.012 (0.010)

0.007 (0.010)

0.008** (0.011)

0.006 (0.005)

0.010** (0.004)

0.057*** (0.013)

𝑇𝑅𝐴𝐷𝐸𝐿𝑦2008 0.003 (0.003)

-0.0003 (0.011)

-0.004 (0.013)

-0.15*** (0.004)

-0.004* (0.003)

0.022 (0.023)

𝑇𝑅𝐴𝐷𝐸𝐿𝑦2009 0.007* (0.004)

0.0007 (0.011)

-0.006 (0.012)

-0.009* (0.006)

0.003* (0.002)

0.018 (0.025)

𝑇𝑅𝐴𝐷𝐸𝑈𝑦2008 0.0002 (0.001)

0.013 (0.014)

0.017 (0.011)

0.024*** (0.007)

0.001** (0.0005)

-0.005 (0.006)

𝑇𝑅𝐴𝐷𝐸𝑈𝑦2009 0.001* (0.0006)

0.002 (0.002)

0.004* (0.002)

0.006** (0.003)

0.002*** (0.0007)

0.003 90.007)

𝐸𝑅 -5.13e-07 1.99e-06 -1.24e-06 5.57e-06*** 6.10e-06***

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(0.000) (0.000) (0.000) (0.000) (0.000) 𝐸𝐷𝑈 -0.017

(0.096) 0.017

(0.132) -0.031

(0.096) 0.601* (0.362)

0.719*** (0.205)

𝐺𝑂𝑉𝐸𝑋𝑃 0.001 (0.001)

-0.0004 (0.0006)

0.0002 (0.0005)

-0.001 (0.0007)

0.006*** (0.002)

0.007*** (0.002)

𝐼𝑁𝐹 0.0003 (0.0003)

0.0002* (0.0001)

0.0002 (0.0001)

0.0002 (0.0001)

-0.001 (0.001)

-8.35e-06 (0.001)

𝐼𝑁𝑇𝐸𝑅 -0.001*** (0.0001)

-0.001*** (0.0002)

-0.001*** (0.0002)

0.003*** (0.0004)

0.003*** (0.0005)

𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 0.006*** (0.0007)

0.007*** (0.0007)

0.006*** (0.0006)

0.006*** (0.0006)

0.01*** (0.0007)

0.009*** (0.001)

0.01*** (0.0007)

-0.001 (0.002)

-0.0003 (0.002)

𝑁𝑜 𝑂𝑏𝑠. 160 160 160 160 160 160 160 160 160 𝐹 − 𝑆𝑡𝑎𝑡 131.32 282.46 109.30 98.63 170.02 187.50 233.40 72.16 48.37 𝑅2 0.9026 0.9035 0.9121 0.9164 0.9587 0.9422 0.9530



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Table 8: Panel regression results for the impact of globalization on 𝑃𝐺𝐷𝑃 across income groups

𝑙𝑜𝑔𝑃𝐺𝐷𝑃 (1) (2) (3) (4) (5) (6) (7) (8) (9) 𝐼𝑁𝑇𝐸𝑅𝐶𝐸𝑃𝑇 6.975***

(0.073) 6.532*** (0.188)

6.558*** (0.192)

6.363*** (0.181)

7.743*** (0.819)

8.043*** (1.021)

7.859*** (0.834)

7.938*** (0.569)

7.293*** (0.674)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶 3.125* (1.897)

1.561 (2.069)

1.289 (1.696)

0.032 (1.464)

1.859 (1.550)

-5.276* (3.336)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶 -0.499 (0.664)

-1.225* (0.764)

-0.827 (0.686)

-0.373 (0.634)

0.033 (0.611)

10.198*** (1.569)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶𝑦2008 1.253 (1.073)

1.928 (4.874)

2.668 (5.721)

4.508 (3.373)

1.549 (1.328)

6.586** (2.907)

𝐹𝐷𝐼𝐿𝑀𝐼𝐶𝑦2009 1.407 (1.079)

5.383 (4.428)

6.738* (3.816)

7.37*** (2.528)

1.619** (0.726)

4.537 (3.839)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶𝑦2008 1.617 (2.469)

26.241** (11.715)

23.742* (13.636)

16.852 (12.077)

3.201 (1.953)

18.576*** (6.434)

𝐹𝐷𝐼𝑈𝑀𝐼𝐶𝑦2009 -1.342* (0.594)

0.243 (0.368)

0.201 (1.453)

0.226 (1.057)

-0.51 (0.514)

6.562*** (1.415)

𝑇𝑅𝐴𝐷𝐸𝐿𝑀𝐼𝐶 0.394** (0.139)

0.331* (0.163)

0.255** (0.119)

0.415* (0.252)

0.457* (0.245)

0.264 (0.236)

𝑇𝑅𝐴𝐷𝐸𝑈𝑀𝐼𝐶 0.336*** (0.116)

0.355** (0.128)

0.35** (0.124)

0.247*** (0.079)

0.228*** (0.064)

0.792*** (0.121)

𝑇𝑅𝐴𝐷𝐸𝐿𝑦2008 0.096*** (0.020)

-0.03 (0.259)

-0.073 (0.282)

-0.181 (0.166)

0.063* (0.035)

0.23 (0.175)

𝑇𝑅𝐴𝐷𝐸𝐿𝑦2009 0.093*** (0.023)

-0.185 (0.202)

-0.261 (0.186)

-0.294** (0.118)

0.07 (0.072)

0.293* (0.176)

𝑇𝑅𝐴𝐷𝐸𝑈𝑦2008 -0.008 (0.029)

-0.443** (0.207)

-0.389* (0.236)

-0.25 (0.202)

0.021 (0.027)

-0.049 (0.082)

𝑇𝑅𝐴𝐷𝐸𝑈𝑦2009 -0.026* (0.014)

-0.053** (0.024)

-0.043 (0.056)

-0.024 (0.037)

-0.012 (0.013)

0.013 (0.082)

𝑙𝑜𝑔𝐸𝑅 0.033 0.068* 0.023 0.073** 0.07*

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(0.045) (0.037) (0.041) (0.038) (0.038) 𝑙𝑜𝑔𝐸𝐷𝑈 0.756

(0.489) 0.814

(0.593) 0.802* (0.491)

1.688*** (0.362)

1.753*** (0.346)

𝐺𝑂𝑉𝐸𝑋𝑃 0.019* (0.01)

-0.006 (0.020)

-0.007 (0.021)

-0.006 (0.021)

0.096*** (0.023)

0.105*** (0.026)

𝐼𝑁𝐹 0.002 (0.008)

0.001 (0.006)

0.001 (0.007)

0.001 (0.007)

-0.001 (0.011)

0.009 (0.009)

𝐼𝑁𝑇𝐸𝑅 -0.013** (0.006)

-0.011* (0.006)

-0.013** (0.006)

0.041*** (0.005)

0.032*** (0.005)

𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 0.101*** (0.011)

0.105*** (0.010)

0.104*** (0.011)

0.103*** (0.008)

0.135*** (0.017)

0.126*** (0.018)

0.135*** (0.017)

-0.001 (0.019)

0.027* (0.017)

𝑁𝑜 𝑂𝑏𝑠. 160 160 160 160 160 160 160 160 160 𝐹 − 𝑆𝑡𝑎𝑡 206.82 115.88 83.27 74.13 82.12 112.74 122.95 171.95 80.85 𝑅2 0.8748 0.8822 0.8877 0.8917 0.9180 0.9074 0.9145


Source: Author estimation.IJSER




In terms of applying GDP per capita to measure welfare improvement, table 8 shows the

panel regression results for the impact of globalization on poverty reduction across income

group countries. The effect of 𝐹𝐷𝐼𝐺𝐷𝑃 on welfare is positive in the lower-middle income

group, whereas the impact of this factor is negative in richer countries. However, the impacts

of inward FDI on welfare in both groups are insignificant. With the presence of the financial

crisis in 2008-2009, the effects of FDI inflows on welfare remain positive. Therefore, inward

FDI could create more employment, improve productivity and local labour’s skills, then it

could be a crucial determinant to increase GDP per capita in ASEAN countries. In some

cases, investments of FDI inflows in richer countries are more efficient than those of poorer

countries, then they can contribute more to economic development.

For the impact of international trade, this factor has a positive and significant impact on GDP

per capita at 1%, 5%, and 10% significance level (columns (2), (3), (4), (5) and (7)). The

impacts are almost the same in both income groups. Thus, it can be argued that international

trade has been an important factor in improving economic development as well as welfare

improvement in the ASEAN over the period. However, through the impact of trade, the global

financial crisis has a diverse impact on welfare. The results indicate that the coefficients of the

interaction of trade and time dummy have both positive and negative signs in the model.

Regarding the impacts of control variables, the exchange rate and education have a positive

impact on welfare at a 10% significance level (columns (6) and (7)). In addition, depreciation

of domestic currency against foreign currency could promote exports of goods and services

and they can contribute more to economic development. Similarly, a higher level of the

qualified educated population could improve welfare. Government spending and

improvement of infrastructure system have a negative impact on poverty reduction (columns

(5), (6), and (7)). These effects are opposite to the expectation of fiscal policy and better

infrastructure system in developing the economy. Nonetheless, they have a positive and

significant impact on welfare at a 1% significance level (columns (8) and (9)). In addition, it

is witnessed that GDP per capita also increases over the period in this model.

5. Conclusion and policy recommendations

This paper examines the globalization-poverty reduction nexus through the impact of FDI

inflows and international trade on welfare across the ASEAN countries. The main focuses of

this study are the impacts of inward FDI and international trade on poverty reduction in the

ASEAN. The paper applies HDI and GDP per capita as dependent variables to measure the

improvement of welfare in the contexts of other control variables derived from globalization.

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To investigate the effects of inward FDI and international trade on welfare, the paper uses the

fixed effects method to estimate the panel regression models. The models also apply time

dummy as a variable to control the effect of the global financial crisis over the period.

On the basis, the findings of the paper demonstrate that there is a strong positive relationship

between globalization in terms of FDI inflows and international trade and welfare

improvement in the ASEAN as a whole, and across income group countries. The impact of

inward FDI on poverty is greater in lower-middle income group than that of upper-middle

income group country. This means that the poorer countries benefit more from FDI inflows

than the richer countries. The results of the regressions depict the statistically significant

impact of FDI inflows on welfare at 1% and 5% significance level before and after controlling

for the official exchange rate, education, government spending, macroeconomic instability,

the number of the Internet users, time dummy for the global financial crisis, and time trend.

The study found that international trade has a lower impact on poverty reduction compared to

FDI inflows. Moreover, during the global financial crisis, the impact of inward FDI and trade

on welfare are diverse. This means that the crisis has some impacts on poverty reduction

which could be negative and positive across the ASEAN countries. Furthermore, welfare

improvement has increased over the period. This means that human development and living

conditions have been significantly improved in the last decade.

Three main policy recommendations can be drawn from the empirical results. Firstly, a

recommendation on FDI policy should be made. The ASEAN countries should continue to

create appropriate incentives in order to attract more FDI from foreign countries. A higher

level of FDI inflows with efficient investment would lead to higher level of welfare

improvement due to the transfer of capital, advanced technologies, management skills, and

other benefits from inward FDI to the host countries. These investments could create more

employment, develop skills of local labours, promote technological progress, and thus

improve welfare in the whole ASEAN region. The FDI policies should aim at regional

development, the most productive sectors of the economy, and encourage FDI into labour

intensive and pro-poor sectors such as agriculture, education, and infrastructure development.

Secondly, some studies also confirm that the economy would benefit from trade due to the

expansion of the markets and the availability of products from international trade. Thus, the

ASEAN countries should form appropriate trade policies so as to expand the markets for the

production of the economy as well as promote exports to the developed countries’ market.

Finally, the ASEAN countries should accelerate the implementation of investment

liberalization agreements and other economic treaties internally and externally, and the

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expansion of economic integration to the external regions. This policy not only moves up the

regional value chain in the ASEAN but also stimulates FDI inflows and the degree of

openness to trade so as to increase welfare improvement. Of course, macroeconomic stability,

the high quality of education, adequate infrastructure system should be taken into account to

generate attractive business environments to attract more inward FDI and stimulate

international trade.

For this paper, due to the data limitations, the impact of globalization in terms of FDI inflows

and international trade on welfare is not investigated clearly. In addition, globalization not

only results in some benefits but also generate some disadvantages such as inequality. Hence,

the globalization-poverty reduction nexus related to inequality in the case of the ASEAN

countries have not investigated in this paper. This interesting and important issue will be

assessed in the future research.

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References

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Association of Southeast Asian Nations 2017, ‘Overview of ASEAN’, Association of Southeast Asian Nations, viewed 12 Septem 2017, <http://asean.org/asean/about-asean/overview/>.

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Poverty Reduction Nexus: The Case of the ASEAN - …...and the extension of economic integration so that each nation is able to stimulate FDI inflows and the degree of openness so